The National Library of Medicine (NLM) is developing a digital chest x-ray (CXR) screening system for deployment in resource constrained communities and developing countries worldwide with a focus on early detection of tuberculosis. A critical component in the computer-aided diagnosis of digital CXRs is the automatic detection of the lung regions. In this paper, we present a non-rigid registration-driven robust lung segmentation method using image retrieval-based patient specific adaptive lung models that detects lung boundaries, surpassing state-of-the-art performance. The method consists of three main stages: (i) a content-based image retrieval approach for identifying training images (with masks) most similar to the patient CXR using a partial Radon transform and Bhattacharyya shape similarity measure, (ii) creating the initial patient-specific anatomical model of lung shape using SIFT-flow for deformable registration of training masks to the patient CXR, and (iii) extracting refined lung boundaries using a graph cuts optimization approach with a customized energy function. Our average accuracy of 95:4% on the public JSRT database is the highest among published results. A similar degree of accuracy of 94:1% and 91:7% on two new CXR datasets from Montgomery County, Maryland (USA) and India, respectively, demonstrates the robustness of our lung segmentation approach.

One of the first steps of computer-aided systems is robustly detect the anatomical boundaries. Literature has several successful energy minimization based algorithms which are applied to medical images. However, these algorithms depend on parameters which need to be tuned for a meaningful solution. One of the important parameters is the regularization parameter (lambda) which is generally estimated in an ad-hoc manner and is used for the whole data set. In this paper we claim that lambda can be learned by local features which hold the regional characteristics of the image. We propose a lambda estimation system which is modeled as a multi-class classification scheme. We demonstrate the performance of the approach within graph cut segmentation framework via qualitative results on chest X-rays. Experimental results indicate that predicted parameters produce better segmentation results.